Division of Informatics
University of Edinburgh
Contents
Past Meetings
Pages maintained
by Amos Storkey
Past Meetings
1999
Friday 5 February
Boyen & Koller (1998), Tractable inference for Complex Stochastic Processes
Proceedings of the Fourteenth Annual Conference on Uncertainty in AI, 33-42.Introduced by Amos Storkey
Friday 19 February
Ghahramani, Z. and Jordan, M.I. (1997) Factorial Hidden Markov Models , Machine Learning 29, 245-273.Introduced by Will Lowe
Friday 5 March (NOTE MEETING TIME IS 1.00pm THIS WEEK)
J.F.G. de Freitas, M. Niranjan, A.H. Gee and A. Doucet (1998)
Sequential Monte Carlo Methods for Optimisation of Neural Network Models. Technical report CUED/F-INFENG/TR 328 ,
Cambridge University, Department of Engineering, July 1998.Introduced by Matthias Seeger.
Friday 19 March
M. Ostendorf and V. Digalakis and O. Kimball (1996)
From HMMs to Segment Models: A Unified View of Stochastic Modeling for Speech Recognition (Local copy)
IEEE Trans. on Speech and Audio Processing 4, 360-378Introduced by Simon King
Friday 2 April - This is Good Friday. No meeting this week.
Friday 16 April: Postponed to Friday 23 AprilFriday 23 April
Learning multi-class dynamics, Andrew Blake, Ben North and Michael Isard. Advances in Neural Information Processing Systems 11, in press, MIT Press, (1999).Introduced by Stephen Isard
Friday 7 May
An introduction to the junction tree algorithm: discussion.Friday 21 May
David MacKay (1998) Introduction to Monte Carlo Methods (appears in Learning in Graphical Models, ed. M. I. Jordan, Kluwer 1998)Introduced by Chris Williams
Friday 11 June
F. C. N. Pereira (1999) Speech Recognition by Composition of Weighted Finite Automata.Introduced by Paul Taylor
Friday 15 October
Geoffrey Hinton (1999) Products of Experts. ICANN99.Introduced by Paul Taylor
Friday 29 October
A presentation by William Chesters
Friday 12 November
Te-Won Lee, Mark Girolami, Anthony Bell and Terrence Sejnowski A Unifying Information-theoretic Framework for Independent Component Analysis.Introduced by Stephen Felderhof
Friday 26 November
Dellaportas, Forster, and Ntzoufras On Bayesian Model and Variable Sepection using MCMCIntroduced by Joe Frankel
Friday 10 December
Andreas Stolcke An efficient probabilistic context-free parsing algorithm that computes prefix probabilities, Computational Linguistics 21(2), 165-201.Introduced by Chris Brew
2000
Friday 21 January
Note: This meeting will be held at 12:30 Faculty Room North, David Hume Tower.The first meeting of this term will involve a talk rather than a paper discussion (non-PMRG members very welcome):
Sam Roweis from the Gatsby Computational Neuroscience Group at UCL, London will be talking on
Constrained Hidden Markov Models for Sequence Modeling By thinking of each state in a hidden Markov model as corresponding to some spatial region of a fictitious "topology space" it is possible to naturally define the neighbouring states of any state as those which are connected in that space. The transition matrix of the HMM can then be constrained to allow transitions only between neighbours; this means that all valid state sequences correspond to connected paths in the topology space. This strong constraint makes structure discovery in sequences easier. I show how such *constrained HMMs* can learn to discover underlying structure in complex sequences of high dimensional data, and apply them to the problem of recovering mouth movements from acoustic observations in continuous speech and to learning character sequences in text.
Fiday 3 March
Applying Collins' Models for Categorial GrammarsJulia Hockenmaier will be introducing her work. She has suggested discussing the paper "Three Generative, Lexicalised Models for Statistical Parsing", by Michael Collins; which can be found at http://xxx.lanl.gov/abs/cmp-lg/9706022 She will introduce this paper and say how it relates to her work.
Friday 12 May
Exact SamplingI will set the ball rolling this term with
P.J. Green and Duncan J. Murdoch (1998) Exact Sampling for Bayesian Inference: Towards general purpose algorithms.
From this I hope we will get some overview of exact sampling. For those who want more detail I have also provided the Propp and Wilson reference here (quite long).
Friday 8 September
The first meeting of term will involve looking at two papers. The first is a short tutorial:Adam Berger "A gentle introduction to iterative scaling" available from http://www.cs.cmu.edu/People/aberger/maxent.html
which will then lead on to wider discussion regarding
S. Della Pietra, V. Della Pietra, and J. Lafferty, Inducing features of random fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4), April 1997, pp. 380-393. available from http://www.cs.cmu.edu/~lafferty/pubs.html. See also Lafferty's home page
Chris Williams will be introducing this session.
Friday 22 September
We will be discussingNaftali Tishby, Fernando Pereira, and William Bialek (1999) The Information Bottleneck Method. Invited paper to the 37th annual Allerton Conference on Communication, Control, and Computing. 10 pages.
available from
http://www.cs.huji.ac.il/labs/learning/Papers/MLT_list.html Matthias Seeger will be introducing the paper.
Friday 6 October
We will be discussingMike Schuster (2000) Better Generative Models for Sequential Data Problems: Bidirectional Recurrent Mixture Density Networks. NIPS1999. This paper is not available in the NIPS online, and so the copy here is a pdf scanned version, and hence suffers from some degradation. It is generally readable, but those with access to NIPS proceedings will probably want to use their "home grown" copies.
Simon King will be introducing this paper.